ANN and SVM Based War Scene Classification Using Invariant Moments and GLCM Features: A Comparative Study

S. Daniel Madan Raja and A. Shanmugam

Abstract—Scene classification underlies many problems in
visual perception such as object recognition and environment
navigation. In this paper we are trying to classify a war scene
from the natural scene. For this purpose two set of image
categories are taken viz., opencountry & war tank. By using
Invariant Moments and Gray Level Co-occurrence Matrix
(GLCM), features are extracted from the images. The extracted
features are trained and tested with (i) Artificial Neural
Networks (ANN) using feed forward back propagation
algorithm and (ii) Support Vector Machines (SVM) using radial
basis kernel function with p=5. The comparative results are
proving efficiency of Support Vector Machines towards war
scene classification problems by using GLCM feature
extraction method. Although this study has been the first step of
the research in the area of scene classification, the results have
shown that the war scenes can be successfully classified from
opencountry. It can be concluded that the proposed work
significantly and directly contributes to scene classification and
its new applications. The complete work is experimented in
Matlab 7.6.0 using real world dataset.